The 1928 platform

A user-friendly flow providing:

Epidemiologic typing

Antibiotic resistance profiling

Virulence factor profiling

in minutes

Our CTO Fredrik Dyrkell about the 1928 platform

Cloud service platform for microbial analysis

1928 delivers a cloud service that analyze resistance markers in bacteria from whole genome sequences.
The software looks for resistance markers within the bacteria and do relational analysis between samples.

From raw reads to result in minutes

You start by uploading the sample to our cloud platform and the analysis starts automatically.
Your NGS raw data is transcribed to results, providing an understanding of both the unique in the sample and overall picture in between samples.

Start today

There are already several hospitals in Europe and the USA that are getting value out of the 1928 platform.
So if you are starting to do WGS, we will be happy to provide the analysis for you.

Infection control

1928 enables comprehensive and in-depth outbreak tracing from your sequencing data providing strong infection control which ultimately prevents outbreaks. By clustering on cgMLST differences, high resolution comparisons are made of different isolates. The results are visualised in an easy-to-interpret phylogenetic tree.

Prof. Gunnar Kahlmeter on the future of infection control

The source of hospital-acquired infections is really us, the people

The patients, the hospital staff, we all carry bacteria. And people need to know about the dangers and how to behave to prevent spreading infections.
To be proactive in your infection control, irrespective of what healthcare system you’re working in, you have to make sure that the whole chain of events is carefully planned and organized.

Everyone can support the fight against antibiotic resistance

It’s one of those situations where we all have responsibility.
It’s a bit like the climate catastrophe, everyone has to do their part and that part may seem small but unless we put all those small parts together, we will not succeed.

1928DSA - full control of MRSA

Methicillin-resistant Staphylococcus aureus (MRSA) is the most common cause
of hospital-acquired infections (HAIs) causing thousands of deaths in hospitals
worldwide. 1928DSA recieved its -marking in 2018, it's a
high quality analysis product for Staphylococcus aureus WGS samples, that
supplies a resistance profile for each uploaded sample by matching it to our manually
curated database. The result has a very high accuracy in predicting resistance (see table below)
and can be used to guide treatment.

1928DSA antibiotic accuracy

Antibiotic

Major error rate

Very major error rate

Ciprofloxacin

0,3%

1,0%

Clindamycin

0,7%

0,0%

Erythromycin

0,1%

0,1%

Fusidic Acid

0,5%

0,5%

Gentamicin

0,0%

0,1%

Mupirocin

0,4%

0,0%

Penicillinase-labile penicillins

1,0%

1,3%

Isoxazolyl Penicillins (MRSA)

0,3%

0,0%

Rifampicin

0,1%

0,0%

Tetracycline

0,1%

0,0%

Trimethoprim

0,0%

1,0%

Vancomycin

0,0%

0,0%

Methodology

Antibiotic resistance profiling and virulence factor detection

The 1928 platform contains well-validated and specially adapted algorithms that processes the raw data file from the sequencing machine. By optimising data handling processes in the cloud, the calculations are always efficient and fast due to optimised workflows and use of distributed systems.
The processed data is matched to our databases of genetic markers (genes and mutations) coding for antibiotic resistance or virulence factors. The database entries are collected from peer reviewed scientific journals and comprise clinically relevant markers that are carefully selected and manually curated.
The result is delivered on the platform in an informative format and can also be exported.

Epidemiologic typing

1928 uses core genome multilocus sequence typing (cgMLST) for outbreak tracing. This method is robust and enables strong comparability between sample sets. We generate our own cgMLST schemes which consists of conserved genes that can be used to generate a "bacterial fingerprint". By visualising the number of identical genes found in phylogenetic trees large sample sets can be compared and groups of closely related samples can be identified as outbreaks. The method also allows for new samples to be compared to historical data previously uploaded.